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[Env] Game of Life Halting Prediction (#272)

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This is a variant of the Game of Life task, which rather than trying to test the algorithmic simulation, tests the ability of the model to do explanatory reasoning of the board. The idea is that a model with good explanatory reasoning will be able to see that a game will not halt without simulating it into the future.

The task presents a GoL board, and the model is asked to predict if the board will halt (die, all cells zero) after n steps. Sometimes, the board will be made up of 'oscillators', isolated structures which never die. Othertimes, it is filled with non-oscillators, structures which will always die after a few steps. The model should deduce which case the presented board is.
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Rich Jones 2025-03-07 10:05:12 +01:00 • committed by GitHub
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reasoning_gym/algorithmic/__init__.py
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@ -14,6 +14,7 @@ from .caesar_cipher import CaesarCipherConfig, CaesarCipherDataset
from .count_primes import CountPrimesConfig, CountPrimesDataset
from .cryptarithm import CryptarithmConfig, CryptarithmDataset
from .game_of_life import GameOfLifeConfig, GameOfLifeDataset
from .game_of_life_halting import GameOfLifeHaltingConfig, GameOfLifeHaltingDataset
from .graph_color import GraphColorConfig, GraphColorDataset
from .group_anagrams import GroupAnagramsConfig, GroupAnagramsDataset
from .isomorphic_strings import IsomorphicStringsConfig, IsomorphicStringsDataset
@ -51,6 +52,8 @@ __all__ = [
"CryptarithmDataset",
"GameOfLifeConfig",
"GameOfLifeDataset",
"GameOfLifeHaltingConfig",
"GameOfLifeHaltingDataset",
"LetterCountingConfig",
"LetterCountingDataset",
"LetterJumbleConfig",